wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-29 08:49:15 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add bandwidth parameter to gabor_kernel

Browse Source 
Note that this changes the API of `gabor_kernel` and `gabor_filter`: The input parameters are rearranged and positional arguments are changed to keyword args.
This commit is contained in:
Tony S Yu
2013-04-06 21:18:53 -05:00
parent 7d407fc778
commit 2bf178ceb2
2 changed files with 59 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/filter/_gabor.py
+34 -12
View File
@@ -2,7 +2,17 @@ import numpy as np
from scipy import ndimage
def gabor_kernel(sigma_x, sigma_y, frequency, theta, offset=0):
__all__ = ['gabor_kernel', 'gabor_filter']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2)/2.0) * (2.0**b + 1) / (2.0**b - 1)
def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
offset=0):
"""Build complex 2D Gabor filter kernel.
Frequency and orientation representations of the Gabor filter are similar
@@ -11,14 +21,18 @@ def gabor_kernel(sigma_x, sigma_y, frequency, theta, offset=0):
Parameters
----------
frequency : float
Frequency of the harmonic function.
theta : float
Orientation in radians. If 0, the harmonic is in the x-direction.
bandwidth : float
The bandwidth captured by the filter. For fixed bandwidth, `sigma_x`
and `sigma_y` will decrease with increasing frequency. This value is
ignored if `sigma_x` and `sigma_y` are set by the user.
sigma_x, sigma_y : float
Standard deviation in x- and y-directions. These directions apply to
the kernel *before* rotation. If `theta = pi/2`, then the kernel is
rotated 90 degrees so that `sigma_x` controls the *vertical* direction.
frequency : float
Frequency of the harmonic function.
theta : float
Orientation in radians.
offset : float, optional
Phase offset of harmonic function in radians.
@@ -33,6 +47,10 @@ def gabor_kernel(sigma_x, sigma_y, frequency, theta, offset=0):
.. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf
"""
if sigma_x is None:
sigma_x = _sigma_prefactor(bandwidth) / frequency
if sigma_y is None:
sigma_y = _sigma_prefactor(bandwidth) / frequency
n_stds = 3
x0 = np.ceil(max(np.abs(n_stds * sigma_x * np.cos(theta)),
@@ -52,8 +70,8 @@ def gabor_kernel(sigma_x, sigma_y, frequency, theta, offset=0):
return g
def gabor_filter(image, sigma_x, sigma_y, frequency, theta, offset=0,
mode='reflect', cval=0):
def gabor_filter(image, frequency, theta=0, bandwidth=1, sigma_x=None,
sigma_y=None, offset=0, mode='reflect', cval=0):
"""Perform Gabor filtering.
The real and imaginary parts of the Gabor filter kernel are applied to the
@@ -65,14 +83,18 @@ def gabor_filter(image, sigma_x, sigma_y, frequency, theta, offset=0,
Parameters
----------
frequency : float
Frequency of the harmonic function.
theta : float
Orientation in radians. If 0, the harmonic is in the x-direction.
bandwidth : float
The bandwidth captured by the filter. For fixed bandwidth, `sigma_x`
and `sigma_y` will decrease with increasing frequency. This value is
ignored if `sigma_x` and `sigma_y` are set by the user.
sigma_x, sigma_y : float
Standard deviation in x- and y-directions. These directions apply to
the kernel *before* rotation. If `theta = pi/2`, then the kernel is
rotated 90 degrees so that `sigma_x` controls the *vertical* direction.
frequency : float
Frequency of the harmonic function.
theta : float
Orientation in radians.
offset : float, optional
Phase offset of harmonic function in radians.
@@ -89,7 +111,7 @@ def gabor_filter(image, sigma_x, sigma_y, frequency, theta, offset=0,
"""
g = gabor_kernel(sigma_x, sigma_y, frequency, theta, offset)
g = gabor_kernel(frequency, theta, bandwidth, sigma_x, sigma_y, offset)
filtered_real = ndimage.convolve(image, np.real(g), mode=mode, cval=cval)
filtered_imag = ndimage.convolve(image, np.imag(g), mode=mode, cval=cval)
skimage/filter/tests/test_gabor.py
+25 -11
View File
@@ -2,7 +2,7 @@ import numpy as np
from numpy.testing import (assert_equal, assert_almost_equal,
assert_array_almost_equal)
from skimage.filter import gabor_kernel, gabor_filter
from skimage.filter._gabor import gabor_kernel, gabor_filter, _sigma_prefactor
def test_gabor_kernel_size():
@@ -12,21 +12,35 @@ def test_gabor_kernel_size():
size_x = sigma_x * 6 + 1
size_y = sigma_y * 6 + 1
theta = 0
kernel = gabor_kernel(sigma_x, sigma_y, 0, theta)
kernel = gabor_kernel(0, theta=0, sigma_x=sigma_x, sigma_y=sigma_y)
assert_equal(kernel.shape, (size_y, size_x))
theta = np.pi / 2
kernel = gabor_kernel(sigma_x, sigma_y, 0, theta)
kernel = gabor_kernel(0, theta=np.pi/2, sigma_x=sigma_x, sigma_y=sigma_y)
assert_equal(kernel.shape, (size_x, size_y))
def test_gabor_kernel_bandwidth():
kernel = gabor_kernel(1, bandwidth=1)
assert_equal(kernel.shape, (5, 5))
kernel = gabor_kernel(1, bandwidth=0.5)
assert_equal(kernel.shape, (9, 9))
kernel = gabor_kernel(0.5, bandwidth=1)
assert_equal(kernel.shape, (9, 9))
def test_sigma_prefactor():
assert_almost_equal(_sigma_prefactor(1), 0.56, 2)
assert_almost_equal(_sigma_prefactor(0.5), 1.09, 2)
def test_gabor_kernel_sum():
for sigma_x in range(1, 10, 2):
for sigma_y in range(1, 10, 2):
for frequency in range(0, 10, 2):
kernel = gabor_kernel(sigma_x, sigma_y, frequency+0.1, 0)
kernel = gabor_kernel(frequency+0.1, theta=0,
sigma_x=sigma_x, sigma_y=sigma_y)
# make sure gaussian distribution is covered nearly 100%
assert_almost_equal(np.abs(kernel).sum(), 1, 2)
@@ -36,17 +50,17 @@ def test_gabor_kernel_theta():
for sigma_y in range(1, 10, 2):
for frequency in range(0, 10, 2):
for theta in range(0, 10, 2):
kernel0 = gabor_kernel(sigma_x, sigma_y, frequency+0.1,
theta)
kernel180 = gabor_kernel(sigma_x, sigma_y, frequency,
theta+np.pi)
kernel0 = gabor_kernel(frequency+0.1, theta=theta,
sigma_x=sigma_x, sigma_y=sigma_y)
kernel180 = gabor_kernel(frequency, theta=theta+np.pi,
sigma_x=sigma_x, sigma_y=sigma_y)
assert_array_almost_equal(np.abs(kernel0),
np.abs(kernel180))
def test_gabor_filter():
real, imag = gabor_filter(np.random.random((100, 100)), 1, 1, 1, 1)
real, imag = gabor_filter(np.random.random((100, 100)), 1)
if __name__ == "__main__":